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@snim2 snim2/interp.py
Last active Aug 29, 2015

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Pylab interpolation example
{
"metadata": {
"name": "Heatmap example"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "%matplotlib inline\nimport pylab\nresults = [[3.1, 4.1, 5.9, 2.6],\n [5.3, 5.8, 9.7, 9.3],\n [2.3, 6.4, 1.4, 1.4],\n [1.7, 2.9, 6.8, -1.0],]\npylab.figure()\npylab.grid(True)\npylab.title('A nice caption which explains the figure should go here.\\n')\npylab.xlabel('X axis label')\npylab.ylabel('Y axis label')\npylab.xlim(-0.5, 3+0.5)\npylab.ylim(-0.5, 3+0.5)\nfig = pylab.matshow(results, interpolation='nearest', cmap='Greys', fignum=0)\npylab.colorbar(fig)\n",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": "<matplotlib.colorbar.Colorbar instance at 0x7f73c73ba0e0>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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nqOwgMqr58o1qvvJD9/MlhBAJ0L0diEao5ss3qvnKjxzvWKYJSr6EEK7JsaSg\nCT7H6xyjmi/fqOYrP9q4wk0KNPIFUF1dLVpftbW1ovb3+eefi9YXAJSUlCAzM1O0/lauXClaXwCQ\nkpKC0aNHi9bfX3/9JVpfxcXFovanLbyOfCn5imzEiBFSh6BT9vb2UoegU2ImXrHxum+UfAkhRAK8\nJl/5FUL03NmzZ6UOQadKSkqkDkGnUlJSpA5BZ3jdN5rnSwghEpDjh2maoOQrMqr58o3XuqgmeN03\nOY5qNUHJlxDCNV6TL5/jdY5RzZdvvNZFNcHrvtE8X0IIkQCvI19KviKjmi/feK2LaoLXfaPkSwgh\nEpBjSUETfEbNMar58o3XuqgmeN03mudLCCESkGNi1QQlX5FRzZdvvNZFNcHrvlHyJYQQCfCafKnm\nKzKq+fKN17qoJnjdN6r5EkKIBOSYWDVByVdkVPPlG691UU3wum801YwQQiTQk7JDRUUFZs2aBQ8P\nD3h6eqotvSxatAiDBw+Gj48P0tPTtRY3JV+RUc2Xb7zWRTXB6771JPm++OKLCAsLw4ULF3Du3Dl4\neHiorE9MTMTFixeRm5uLLVu2IDo6WmtxU/IlhHCtu8n3xo0bOHnyJCIjIwEAhoaGMDc3V2mTkJCA\nuXPnAgACAgJQUVGB0tJSrcRNyVdkVPPlG691UU3wum/dTb6XL1+GtbU1nn76afj7+2PBggWoqalR\naVNUVAQnJydh2dHREYWFhVqJm5IvIYRrHSXb4uJinDlzRvhpq6mpCWlpaVi4cCHS0tJgYmKC9957\nr107xli7/rSBkq/IqObLN17roprgdd86Sr6Ojo4ICAgQftpydHSEo6MjRo4cCQCYNWsW0tLSVNo4\nODigoKBAWC4sLISDg4NW4tbr5FtQUIDx48dj2LBh8PLywrp166QOiRCiZd29mbqdnR2cnJyQk5MD\nAEhKSsKwYcNU2kyfPh07d+4EcOvFycLCAra2tlqJW6/n+RoZGWH16tXw9fVFdXU1hg8fjvvvv7/d\nJ5piopov33iti2qC133rSRlg/fr1eOKJJ9DQ0ABXV1ds27YNmzdvBgBERUUhLCwMiYmJcHNzg4mJ\nCbZv366tsPU7+drZ2cHOzg4A0LdvX3h4eKC4uFjS5EsI0a6eJF8fH5929eCoqCiV5Q0bNnR7+53R\n67JDa3l5eUhPT1db+xET1Xz5xmtdVBO87hvd20HGqqurMWvWLKxduxZ9+/Ztt37ZsmUYMGAAgFsj\n5KFDhwqjtepXAAAWaElEQVTlgZZkqa3lP//8U6vb62q5JRm2lAN0vVxWViZqfy0Jo+Uts66Xs7Ky\nRO1Pn5ZTUlJw4MABALc+7NIWOSZWTShY23kUeqaxsRHTpk1DaGgoFi9e3G69QqHQ69Gort4yycXS\npUulDoF0k6ura7tpXLdLoVDglVde0ajtRx991OP+tEnWI9/w8PAO1ykUCiQkJHT6eMYY5s+fD09P\nT7WJlxDCP15vrCPr5NvZK5ombzV++eUX7N69G/feey/8/PwAAO+++y6mTJmitRhv19mzZ/V6xkNJ\nSYlez3hISUnhdlZAV3jdN0q+OhASEiL8XlNTg4KCAgwdOlTjx48bNw7Nzc06iIwQIhe81ny5eMlI\nSEiAn58fJk+eDABIT0/H9OnTJY6qe/R51AvQPF+e8bpvvM524CL5xsXFITU1FZaWlgAAPz8//PXX\nXxJHRQiRA0q+OmRkZAQLCwuVv/Fa59HnmRUAzfPlGa/7xmvylXXNt8WwYcPwxRdfoKmpCbm5uVi3\nbh0CAwOlDosQIgNyTKya4GL4uH79evzxxx+4++67MXv2bJiZmWHNmjVSh9UtVPPlG691UU3wum80\n8tUhExMTrFy5EkuWLIFCoYCZmZnUIRFCZILXEiQXUZ85cwbe3t6499574e3tDR8fH25rp7zGrSmq\n+fKL133r7i0lpcbFyDcyMhKffPIJgoKCAAA///wzIiMjce7cOYkjI4RITY4lBU1wkXwNDQ2FxAvc\nunjC0JCL0Nuhmi/feK2LaoLXfaPkqwO//fYbACA4OBhRUVGYPXs2AGDfvn0IDg6WMjRCiExQ8tWB\nV155RTiwjDG89dZbwu+8HnC6twPfeL3/gSZ43Tdec4Gsk29ycrLUIRBCZE6OH6ZpQtbJt7VDhw4h\nKysLdXV1wt/efPNNCSPqHn0e9QJU8+UZr/tGI18dioqKQm1tLY4dO4YFCxbgyy+/lPzrgAgh8sBr\n8uVivH7q1Cns3LkTVlZWWLZsGVJSUoSv4+ENzfPlG69zYTXB677RFW461KdPHwCAsbExioqK0K9f\nP1y9elXiqAghcsBrzZeLqKdNm4by8nL861//wvDhw+Hi4iJMO+MN1Xz5xmtdVBO87ltPR75KpRJ+\nfn5qv7YsOTkZ5ubm8PPzg5+fH9555x2txc3FyLflg7WZM2di6tSpqKura3eLSULInamnJYW1a9fC\n09MTVVVVatcHBwd3+X2R3SHr5HvgwAGVeb5tD/LDDz8sRVg9QvN8+cbrXFhN8LpvPSk7FBYWIjEx\nEW+88Qb+85//qG2jq288lnXyPXjwYKevajwmX0KIdvVk5PvSSy/hgw8+QGVlZYfbPnXqFHx8fODg\n4IAPP/wQnp6e3e6vNVkn3x07dkgdgtbp86gXoJovz3jdt46Sb25uLnJzczt83KFDh2BjYwM/P78O\nL+jy9/dHQUEBjI2NceTIEcyYMQM5OTnaCFveyZcQQrrSUfIdMmQIhgwZIiwfPXpUZf2pU6eQkJCA\nxMRE1NXVobKyEk899RR27twptDE1NRV+Dw0NxcKFC1FWVgYrK6sex83FbAd9QvN8+cbrXFhN8Lpv\n3Z3tsHLlShQUFODy5cuIj4/HhAkTVBIvAJSWlgo139OnT4MxppXEC9DIFwDw3XffidZXXl4eioqK\nROtP7FkhVVVVovbp6uoqWl9SMDY2Fq0vpVKJXr16idaftmjrAoqW7WzevBnArStrv/rqK2zcuBGG\nhoYwNjZGfHy8VvoCAAXT1Ud5WrR//35MmTIFZmZmWL58OdLS0rB06VL4+/v3eNsKhQJLly7VQpTy\n1NH0GX3B63f5aUrM5Cu2mpqaHs8kUCgU2Lhxo0Zto6OjdTZzoTu4KDssX74cZmZm+Pnnn/Hjjz9i\n/vz5iI6OljosQogM8Po1QvKLSI2Wt0KHDh3CggULMG3aNDQ0NEgcVffk5eVJHYJOFRQUSB0C6Sal\nUil1CN3C670duEi+Dg4OePbZZ7Fv3z7hCrfm5mapwyKEyACNfHVo//79mDx5Mr7//ntYWFigvLwc\nH3zwgdRhdYuLi4vUIeiUk5OT1CGQbuLxwzaA35GvrGc7VFZWwszMDPX19Rg/fjwAoKysDHfffbfe\nX6xACNGMHBOrJmSdfGfPno3Dhw/D399f7QG+fPmyBFH1TF5enl6PfgsKCmj0y6k7faqZ2GSdfA8f\nPgxA/z+kIoR0H6/Jl4ua72effaay3NTUJHyTMW/0edQLUM2XZzyOeoFbcWvyIzdcJN+kpCSEhYWh\nuLgY58+fx5gxYzq8CxEh5M5CH7jp0N69exEfH497770XJiYm+OKLLzBu3Dipw+oWqvkSuaKar7i4\nGPnm5ORg3bp1ePjhh+Hs7Izdu3fj5s2bUodFCJEBXuf5cjHynT59OjZs2IBJkyahubkZq1evxsiR\nI5GVlSV1aLdNn0e9ANV8ecbjqBfgd+TLRfJNTU2Fubk5gFuvcq+88oraL7sjhNx5KPnqkLm5OX7/\n/XdkZWWhrq5OONitb5TMC6r5Ermimq+4uEi+cXFxOH78OP744w9MnToVR44cwbhx4/DUU09JHRoh\nRGI8vmAAnHzg9tVXXyEpKQn29vbYvn07MjMzUVFRIXVY3aLPo16Aar484zWJ0VQzHerTpw969eoF\nQ0ND3LhxAzY2NnTrQkIIAH7LDlyMfEeOHIny8nIsWLAAI0aMgJ+fHwIDA6UOq1v0/VJpelHkF93P\nV1xcjHw/+eQTAMBzzz2HyZMno7KyEj4+PhJHRQiRAznO4dUEF8m3tXvuuUfqEHqEar5Erniu+fKI\nu+RLCCGt8TrylXXUoaGhXN6ztzNU8yVyRTVfcck6+UZGRmLy5MlYsWIFGhsbu70NW1tbeHt7azk6\nQogcdPfeDnV1dQgICICvry88PT0RGxurdvuLFi3C4MGD4ePjg/T0dK3FLeuywyOPPILQ0FC8/fbb\nGDFiBObMmSO8gikUCrz88stdbuPpp59GTEyMbC7IoJovkas7rebbu3dv/PTTTzA2NkZTUxPGjRuH\nn3/+WeWOiYmJibh48SJyc3ORmpqK6OhopKSkaCVuWSdfADAyMkLfvn1RV1eHqqqq267vBAUF6f1b\nfULuZD0pKRgbGwMAGhoaoFQqYWVlpbI+ISEBc+fOBQAEBASgoqICpaWlsLW17X7A/5+sk+/Ro0fx\n8ssvIzw8HOnp6cKB4hnd24HI1Z14b4fm5mb4+/vj0qVLiI6Ohqenp8r6oqIilfPZ0dERhYWF+p98\nV6xYgS+//BLDhg3TaT/fffcdLCwsAAB333037OzshATZMmrW1vLVq1e1ur2ulls+AGs5gXS9fO3a\nNVH703ctH4K1JEWel5VKJZqamgBod3pYR++GMzIykJmZ2eVjMzIycOPGDUyePBnJyckICQlRacMY\nU1nWVuwK1nbLMsIY08qO5uXlITw8HL///nu7dQqFAkuXLu1xH3JVVVUldQg6tWbNGqlD0Cl9eLfX\nkZqamnaJ7XYpFAocO3ZMo7YTJkzotL/ly5ejT58+ePXVV4W/PffccwgJCUFERAQAwN3dHcePH9fK\nyFfWsx3kOD2EECIv3Z1qdv36deEGXbW1tfjhhx/g5+en0mb69OnYuXMnACAlJQUWFhZaSbyAzJOv\nNsyePRuBgYHIycmBk5MTtm/fLmk8+v7hH83z5dedNs+3pKQEEyZMgK+vLwICAhAeHo6JEydi8+bN\n2Lx5MwAgLCwMgwYNgpubG6KiooRbHWiDrGu+2rB3716pQyCE6FB33yF7e3sjLS2t3d+joqJUljds\n2NCt7XdF75Ov3OjzTAfgzvkgTB/xONMB4Lc8ScmXEMI1XpOv3td85YZqvkSu7rSar9Ro5EsI4Zoc\nE6smKPmKjGq+RK6o5isuSr6EEK7xmnyp5isyqvkSuaKar7ho5EsI4ZocE6smKPmKjGq+RK6o5isu\nSr6EEK7xmnyp5isyqvkSuaKar7ho5EsI4ZocE6smKPmKjGq+RK6o5isuSr6EEK7xmnyp5isyqvkS\nuaKar7ho5EsI4ZocE6smKPmKjGq+RK6o5isuSr6EEK519O3Fcsdn1Byjmi+RK15rvryikS8hhGtU\ndiAaoZovkSuq+YqLki8hhGu8Jl+q+YqMar5Ernit+XZ3nm9kZCRsbW3h7e2tdrvJyckwNzeHn58f\n/Pz88M4772g1bhr5AggKChKtLzMzM/j4+IjWX35+vmh9AYChoSHc3d1F6y8mJka0vgCgsLAQjo6O\novUXEREhWl9paWnw9/cXrb+xY8dqZTvdne3w9NNPIyYmBk899VSHbYKDg5GQkNDd0DpFI1+RiZl4\npSBm4pWCmIlXbGImXjkICgqCpaVlp20YYzrrn5IvIYRrurq8WKFQ4NSpU/Dx8UFYWBiysrK0Gjcl\nX5FlZmZKHYJOZWdnSx2CThUWFkodgs6kpaVJHUK36Cr5+vv7o6CgAJmZmYiJicGMGTO0GjfVfAkh\neiklJQWpqandfrypqanwe2hoKBYuXIiysjJYWVlpIzxKvmKjmi/fqOYrPx2NaseMGYMxY8YIy+vX\nr7+t7ZaWlsLGxgYKhQKnT58GY0xriReg5EsI4Vx35/nOnj0bx48fx/Xr1+Hk5IS33noLjY2NAICo\nqCh89dVX2LhxIwwNDWFsbIz4+Hhthk3JV2yZmZl6PfrNzs7W69Gv2FPNxCT2VDNt6W7y3bt3b6fr\nn3/+eTz//PPd2rYmKPkSQrjG6xVulHxFps+jXoBqvjzjcdQL8Jt8aaoZIYRIgJKvyGieL99onq/8\n0He4EUKIBOSYWDVByVdkVPPlG9V85YeSLyGESIDX5Es1X5FRzZdvVPOVH6r5EkKIBOSYWDVBI1+R\nUc2Xb1TzJdpCI19CCNdo5Es0QjVfvlHNV354rflS8iWEEAlQ2UFkVPPlG9V85ae7X6ApNUq+hBCu\nybGkoAk+XzI4RjVfvlHNl2iL3iffo0ePwt3dHYMHD8aqVaukDocQomX0gZsMKZVKvPDCCzh69Ciy\nsrKwd+9eXLhwQdKYqObLN6r5yg8lXxk6ffo03Nzc4OLiAiMjI0REROC7776TOixCCNHv5FtUVAQn\nJydh2dHREUVFRRJGRDVf3lHNV354Hfnq9WwHTQ/4Bx98AFtbWwBA37594erqKpQHWpKltpYvXbqk\n1e11tdySDFvKAbpezs/PF7W/lmTYUg7Q9fK1a9dE7a8lIbaUBHheTktLQ2JiIgDA3t4e2iLHxKoJ\nBWOMSR2ErqSkpCAuLg5Hjx4FALz77rswMDDAkiVLhDYKhQLff/+9VCHqXEsy1Ff6/k4iIiJC6hB0\nZuzYsehp+lEoFLhx44ZGbc3NzXvcnzbpddlhxIgRyM3NRV5eHhoaGrBv3z5Mnz5d6rAIIUS/k6+h\noSE2bNiAyZMnw9PTE4899hg8PDwkjUnfR2pU8+XXnVjz1WQq6qJFizB48GD4+PggPT1da3Hrdc0X\nAEJDQxEaGip1GIQQmWmZipqUlAQHBweMHDkS06dPVxmgJSYm4uLFi8jNzUVqaiqio6ORkpKilf71\neuQrRzTPl280z1d+ujvy1WQqakJCAubOnQsACAgIQEVFBUpLS7USNyVfQsgdSZOpqOraaKv0pPdl\nB7nJzMzU69Fvdna2Xo9+CwsL9Xb0m5aWxuXot6N67okTJ3Dy5MnbflxbbWdIaGtqGyVfQgjXOkqG\nwcHBCA4OFpbfffddlfUODg4oKCgQlgsKCtq9sLZtU1hYCAcHB22ETWUHsenzqBegmi/PeBz19oQm\nU1GnT5+OnTt3Arh13YCFhYVwQVZP0ciXEMK17pYBWk9FVSqVmD9/Pjw8PLB582YAQFRUFMLCwpCY\nmAg3NzeYmJhg+/bt2otbn69w04TYV7iJXfMV+wo3sWu+Ys+bFrvmK+YVbmLXfLV1hVtNTY1GbY2N\njWV1hRuNfAkhXOP13g5U8xUZ1Xz5RjVfoi008iWEcI1GvkQjdG8HvtG9HYi20MiXEMI1GvkSjVDN\nl29U8yXaQiNfQgjXaORLNEI1X75RzZdoC418CSFc43XkS8lXZFTz5RvVfOWH1+RLZQdCCJEAJV+R\nUc2Xb1TzlZ+efIeblCj5iuzSpUtSh6BT+v5V9deuXZM6BJ3Jzc2VOoQ7CtV8RVZdXS11CDpVW1sr\ndQg61dDQIHUIOsPruSnHUa0maORLCCESoJGvyLT1zadydf36dalD0KnKykqpQ9CZkpISqUPoFl5H\nvnf8zdRDQkJw/PhxqcMg5I4THByM5OTkHm3jdhKvpaUlysrKetSfNt3xyZcQQqRANV9CCJEAJV9C\nCJEAJV9CCJEAJV9CCJEAJV+iNQUFBRg0aBDKy8sBAOXl5Rg0aJBWrnobO3asxm1DQkLw22+/ddrG\nxcXltj753rFjB2JiYjRuT0hXKPkSrXFyckJ0dDRee+01AMBrr72GqKgoODs793jbv/zyi8ZtNbmW\nX6FQ4HYm+vA6l5TIFyVfolUvvfQSUlJSsGbNGpw6dQqvvvqq2nYPPfQQRowYAS8vL2zduhUAcOXK\nFQwZMgT//PMPmpubERQUhKSkJABA3759Ady6EOC+++6Dn58fvL298fPPP3caz8KFCzFy5Eh4eXkh\nLi5OZd3777+Pe++9FwEBAcI9N65du4ZZs2Zh1KhRGDVqFE6dOtWTw0FIh+gKN6JVhoaGeP/99xEa\nGooffvgBvXr1Uttu27ZtsLS0RG1tLUaNGoVZs2Zh4MCBWLJkCaKjo4WEOWnSJAD/G3nu2bMHU6ZM\nweuvvw7GGG7evNlpPCtWrIClpSWUSiUmTZqE8+fPw8vLCwBgYWGBc+fOYdeuXVi8eDEOHjyIF198\nES+99BLGjh2L/Px8TJkyBVlZWbc1SiZEE5R8idYdOXIEAwYMwO+//46JEyeqbbN27Vp8++23AG7d\npjEnJwcBAQGYP38+9u/fj82bN6u9/eaoUaMQGRmJxsZGzJgxo8ub0+/btw9bt25FU1MTSkpKkJWV\nJSTf2bNnAwAiIiLw0ksvAQCSkpJw4cIF4fFVVVVdJnhCuoPKDkSrMjIykJSUhF9//RWrV6/G1atX\n27VJTk7Gjz/+iJSUFGRkZMDX1xf19fUAgJqaGhQWFkKhUKCqqqrdY4OCgnDy5Ek4ODhg3rx52LVr\nV4exXL58GR999BGOHTuGzMxMTJ06FXV1dWrbtoysGWNITU1Feno60tPTUVBQABMTE6r5Eq2j5Eu0\nhjGG6OhorF27Fk5OTvjXv/6ltuZbWVkJS0tL9O7dG9nZ2UhJSRHWLVmyBHPmzMFbb72FBQsWtHts\nfn4+rK2t8cwzz+CZZ55Benp6h/FUVlbCxMQEZmZmKC0txZEjR1Ri3bdvH4Bbo+PAwEAAwAMPPIB1\n69YJ7TIyMoT2hGgTJV+iNVu3boWLi4tQali4cCEuXLiAkydPqrSbMmUKmpqa4OnpidjYWIwZMwYA\ncPz4cfz2229YsmQJHn/8cdx11134/PPPAfxvZPrTTz/B19cX/v7+2L9/P1588cUO4/Hx8YGfnx/c\n3d3xxBNPYNy4ccI6hUKB8vJy+Pj4YP369Vi9ejUAYN26dTh79ix8fHwwbNgwbNmyRWhPo1+iTXRj\nHUIIkQCNfAkhRAKUfAkhRAKUfAkhRAKUfAkhRAKUfAkhRAKUfAkhRAKUfAkhRAL/D/u52FLhoLeO\nAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x7f73c7510cd0>"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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